store = {}
store['args']={'name': 'emnist_multibald_bald_k10_706460', 'available_sample_k': 5, 'num_inference_samples': 10, 'seed': 706460, 'acquisition_method': 'AcquisitionMethod.multibald', 'experiment_description': 'EMNIST with b5 and k10, k100 with both BALD and BatchBALD', 'type': 'AcquisitionFunction.bald', 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 16384, 'early_stopping_patience': 3, 'epochs': 40, 'epoch_samples': 20224, 'target_accuracy': 0.85, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.emnist', 'initial_samples': [], 'experiment_task_id': 5, 'experiments_laaos': './experiment_configs/emnist_bbb/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=5', '--experiments_laaos=./experiment_configs/emnist_bbb/configs.py']
store['iterations']=[]
store['initial_samples']=[]
store['iterations'].append({'num_epochs': 0, 'test_metrics': {'accuracy': 0.023351063829787234, 'nll': 3.8639504609209427}, 'chosen_samples': [84786, 14413, 82022, 52923, 108364], 'chosen_samples_score': [0.02544231883480741, 0.049977948545158135, 0.07424278885734203, 0.10074216725503149, 0.1180729082450025], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.045159574468085106, 'nll': 42.67544086212808}, 'chosen_samples': [69842, 52392, 29931, 81355, 40170], 'chosen_samples_score': [1.1491635470365529, 1.7990818576595424, 2.117836270725051, 2.2175167348130187, 2.2638735494017324], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.07425531914893617, 'nll': 34.29351184926134}, 'chosen_samples': [75049, 21609, 49225, 1433, 103229], 'chosen_samples_score': [1.1072306815471566, 1.7014196599471092, 2.0056112803161597, 2.1422381162810367, 2.2037023840210157], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.11377659574468085, 'nll': 32.63286807121114}, 'chosen_samples': [34823, 77680, 42951, 108628, 66217], 'chosen_samples_score': [1.3365664964181634, 2.0805147065789074, 2.2506732789429895, 2.2762567359156987, 2.29639899191453], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.14760638297872342, 'nll': 33.1999511913543}, 'chosen_samples': [56465, 112385, 34554, 11252, 69048], 'chosen_samples_score': [1.4520687677440836, 2.093118690331536, 2.2514860440922613, 2.2936808453527298, 2.2996223894054677], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.15122340425531916, 'nll': 27.7207611506036}, 'chosen_samples': [91159, 20237, 72761, 35747, 66260], 'chosen_samples_score': [1.626958972735559, 2.168007123474445, 2.283160814075615, 2.309872148264854, 2.31550421958425], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.17335106382978724, 'nll': 28.137968266263922}, 'chosen_samples': [18764, 17154, 88854, 7400, 57852], 'chosen_samples_score': [1.5114808132798034, 2.1930029124125974, 2.2877220096238107, 2.3140234106877666, 2.312459652934325], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.17882978723404255, 'nll': 25.2840336657585}, 'chosen_samples': [53365, 49128, 57589, 21215, 37340], 'chosen_samples_score': [1.6440248447717978, 2.1821350155018306, 2.283383373270878, 2.3115246118090433, 2.311298330669408], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.1826063829787234, 'nll': 23.29394275746447}, 'chosen_samples': [39082, 23704, 105302, 66711, 21769], 'chosen_samples_score': [1.6965207269829665, 2.236951647182927, 2.2963157823688265, 2.3124470419405236, 2.307532066324599], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.19579787234042553, 'nll': 20.629137332997423}, 'chosen_samples': [36426, 49884, 93936, 13186, 63423], 'chosen_samples_score': [1.82444814096146, 2.24050185805162, 2.293735367958195, 2.299679594850041, 2.319313944066943], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.22159574468085105, 'nll': 21.492099864229242}, 'chosen_samples': [75195, 23224, 99357, 94075, 66594], 'chosen_samples_score': [1.7651389636174435, 2.2612237338922614, 2.2984638398089166, 2.28690646774895, 2.3187140395566814], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.2255851063829787, 'nll': 17.584936947924025}, 'chosen_samples': [42212, 59588, 106224, 97730, 15084], 'chosen_samples_score': [1.585213665225682, 2.203769109371151, 2.2820854343436103, 2.3060113091233982, 2.2955900824159223], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.23180851063829788, 'nll': 17.43231944632023}, 'chosen_samples': [14178, 4969, 62104, 111762, 89482], 'chosen_samples_score': [1.548965085159867, 2.1672157179420375, 2.281579433583301, 2.3011862208878653, 2.261099626468675], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2378191489361702, 'nll': 16.789899942925636}, 'chosen_samples': [107378, 112249, 107410, 81504, 83297], 'chosen_samples_score': [1.6329583698433345, 2.200449091336181, 2.2794939369988, 2.27285744993621, 2.2939129360362904], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2347872340425532, 'nll': 16.737637519024787}, 'chosen_samples': [98018, 43420, 9751, 88983, 8990], 'chosen_samples_score': [1.7714805364074868, 2.262118548447976, 2.296464898998316, 2.3029749039969962, 2.3000336791797062], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2578191489361702, 'nll': 15.435384990610975}, 'chosen_samples': [104792, 57532, 28794, 60557, 30504], 'chosen_samples_score': [1.5809398567767505, 2.1782034667046837, 2.2816157602680516, 2.3020953759825153, 2.28241546084724], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.26824468085106384, 'nll': 13.225204889013412}, 'chosen_samples': [64325, 49265, 27814, 105809, 82932], 'chosen_samples_score': [1.6999586294699787, 2.2234926889586566, 2.2950902070564765, 2.3093091438846187, 2.3192444503443603], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.2810106382978723, 'nll': 14.824600047659366}, 'chosen_samples': [87650, 83160, 29132, 12763, 73343], 'chosen_samples_score': [1.7087984066745467, 2.2389605404394306, 2.2979648322124495, 2.2797238571465863, 2.324886260701658], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.27930851063829787, 'nll': 12.688466594777209}, 'chosen_samples': [63155, 35451, 41028, 13256, 12093], 'chosen_samples_score': [1.8066553854175988, 2.2860731743498035, 2.301413605494632, 2.321963851237652, 2.304111986179344], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2997872340425532, 'nll': 12.361699318581438}, 'chosen_samples': [10559, 34669, 6320, 101738, 110683], 'chosen_samples_score': [1.733954012397148, 2.2318958314874764, 2.2911560811566005, 2.325167633143203, 2.295904359132443], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3046276595744681, 'nll': 11.513779094777208}, 'chosen_samples': [83448, 42344, 1364, 33657, 94544], 'chosen_samples_score': [1.8547707200098857, 2.287559418348668, 2.300604741577399, 2.2788663460593113, 2.3004990874729785], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3077659574468085, 'nll': 11.317619038845631}, 'chosen_samples': [10814, 111228, 109606, 46387, 33887], 'chosen_samples_score': [1.8523394430873203, 2.2602771910262267, 2.297825239404908, 2.3079219624476193, 2.298688264438607], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3153191489361702, 'nll': 11.576263999127328}, 'chosen_samples': [83905, 105623, 73333, 13566, 59665], 'chosen_samples_score': [1.6775326576107614, 2.250815537247145, 2.2950198236498416, 2.292544956529896, 2.3173489535120058], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.33111702127659576, 'nll': 10.773492876418093}, 'chosen_samples': [110318, 15896, 103941, 23743, 74711], 'chosen_samples_score': [1.7325395573656874, 2.222389767246294, 2.2962887273403334, 2.30754567914207, 2.292355676204103], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.32180851063829785, 'nll': 10.507247997852081}, 'chosen_samples': [88380, 75341, 56627, 13838, 21526], 'chosen_samples_score': [1.833797677381734, 2.247979755491865, 2.293963858350718, 2.321062067361839, 2.3117139749342765], 'chosen_samples_orignal_score': None})
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store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3525, 'nll': 9.260567327458809}, 'chosen_samples': [101801, 73603, 57991, 28512, 65672], 'chosen_samples_score': [1.8874093491142525, 2.28156585716121, 2.299310086029629, 2.3064904085132802, 2.298314056248512], 'chosen_samples_orignal_score': None})
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